As is known, the fact that fingerprints are strictly personal and represent a type of identity card, gives rise to many applications. For example, fingerprints are used conventionally by the police for identification of criminal acts, and it has been proposed to use fingerprints as a type of personal key or signature to improve security of credit cards, to control access to security areas, to computers or to bank transaction systems. Fingerprints can also replace physical keys, which are often cumbersome, in order to obtain access to one's own dwelling or car.
The problem of full identification of fingerprints consists of defining a score to be allocated to a pair of images of fingerprints, which expresses the probability that the two prints belong to the same person. The two prints are acquired at two different moments. One usually belongs to a data bank which contains the reference to known persons, and the other must be validated or recognised by comparison with the first.
Conventional methods for identifying fingerprints, carried out manually by experts, comprise a plurality of classification steps. Recently automatic methods have been proposed, some of which comprise pre-classification steps which are similar to the conventional steps, and others which dispense completely with these stages, and, starting directly with the image of the print obtained provide the desired result, for example through a scanner or sensor, via an appropriate processor. The present invention relates to this second type of approach.
A fully automatic identification system is described for example in the article by K. Asai, Y. Kato, Y. Hoshino, K. Kiji "Automatic Fingerprint Identification," in Proceedings of the Society of Photo-Optical Instrumentation Engineers, vol. 182, Imaging applications for Automated Industrial Inspection and Assembly, pp. 49-56, 1979. According to this system, the notable points on the image (tips and branching of cutaneous crests, known as "minutiae") are determined directly by the image in tones of grey, and the correspondence of the prints is determined by taking into account by counting of the crests and taking into account the direction of the "minutiae," similarly to the manual method.
In D.K. Isenor, S. G. Zaky "Fingerprint Identification Using Graph Matching" Pattern Recognition, Vol. 19, No. 2, pp. 113-122, 1986, a method is described which is based on comparison between graphs. In the image of the fingerprint, the cutaneous crests and indentations are sought, and the former are numbered and oriented, whereas the latter represent the background. A level of adjacency is defined which expresses information concerning which crest is close to which other. On this graph abnormalities are identified and corrected which are associated with dirt or with interruptions of crests, associated with the acquisition system, in order to obtain the final graph which describes the fingerprint in coded form. The comparison between graphs makes it possible to determine the similarity between the fingerprints.
In K. Asai, Y. Hoshino, K. Kiji "Automated Fingerprint Identification by Minutia-network Feature-Matching Processes," Transaction of the Institute of Electronics, Information and Communication Engineers D-II, Vol. J72D-II, No. 5, pp. 733-740, May 1989 (in Japanese), use is made of a minutiae network which contains tip and branching points, and a corresponding link obtained by counting the cutaneous crests between notable adjacent points. There is also consideration of the local direction of the cutaneous crests. The pairs of corresponding points are obtained by transformation of coordinates and calculation of the similarity.
These systems have been assessed on a few hundred proprietary fingerprints, and do not yet permit identification with the accuracy required for police uses, where there are many millions of images to be compared.
In addition, in European patent application 96830080.6 filed on Feb. 2, 1996 in the name of SGS-Thomson Microelectronics, S.r.l., a method of identification is described which is based on the search for characteristic points on the reference image and on the image to be recognised, and on comparison between regions which surround the characteristic points of the two images, on the basis of the integral norm for these regions, carried out through an analog flash memory. This method is applicable in practice in acceptable times, and has provided excellent results concerning the number of incorrect identifications, but the percentage of correct recognition is too low for practical applications. This low level of correct recognition can be attributed to the high deformation which exists between different images of a single print, owing to the possibility of rotations of the print which can be greater than 20.degree., and to the differences in quality of the images, owing to considerable variation of the ink (in excess or lacking). Furthermore, the use of non-linear filtering in the field of frequencies of the images, in order to improve the quality of the image, has led to improvement of the percentage of correct identifications, but is not yet sufficient, and gives rise to a substantial increase in the costs from the point of view of the calculation time.